# Basis Of A Three Dimensional Space

In the preceeding discussion, we talked about the basis of a plane. We can easily extend that discussion to observe that **any three non-coplanar** vectors can form a basis of three dimensional space:

In other words, any vector \(\vec r\) in 3-D space can be expressed as a linear combination of three arbitrary non-coplanar vectors. From this, it also follows that for three non-coplanar vectors \(\vec a,\vec b,\vec c,\) if their linear combination is zero, i.e, if

\[\lambda \vec a + \mu \vec b + \gamma \vec c = \vec 0\quad\qquad\qquad\left( {{\text{where}}\,\,\lambda ,\mu ,\gamma \in \mathbb{R}} \right)\]

then \(\lambda ,\mu \,\,{\text{and}}\,\,\gamma \) must all be zero. To prove this, assume the contrary. Then, we have

\[\vec a = \left( { - \frac{\mu }{\lambda }} \right)\vec b + \left( { - \frac{\gamma }{\lambda }} \right)\vec c\]

which means that \(\vec a\) can be written as the linear combination of \(\vec b\,\,{\text{and}}\,\,\vec c\). However, this would make \(\vec a,\,\,\vec b\,\,{\text{and}}\,\vec c\) coplanar, contradicting our initial supposition. Thus, \(\lambda ,\mu \,\,{\text{and}}\,\,\gamma \) must be zero.

We finally come to what we mean by linearly independent and linearly dependent vectors.

**Linearly independent vectors :** A set of non-zero vectors \({\vec a_1},{\vec a_2},{\vec a_3}....,{\vec a_n}\) is said to be linearly independent if

\[{\lambda _1}{\vec a_1} + {\lambda _2}{\vec a_2} + ... + {\lambda _n}{\vec a_n} = \vec 0\]

\[implies\,\,\,\,{\lambda _1} = {\lambda _2} = .... = {\lambda _n} = 0\]

Thus, a linear combination of linearly independent vectors cannot be zero unless all the scalars used to form the linear combination are zero.

**Linearly dependent vectors: ** A set of non-zero vectors \({\vec a_1},{\vec a_2},{\vec a_3},....,{\vec a_n}\) is said to be linearly dependent if there exist scalars \({\lambda _1},{\lambda _2}....{\lambda _n},\) not all zero such that,

\[{\lambda _1}{\vec a_1} + {\lambda _2}{\vec a_2} + ..... + {\lambda _n}{\vec a_n} = \vec 0\]

For example, based on our previous discussions, we see that

(i) Two non-zero, non-collinear vectors are linearly independent.

(ii) Two collinear vectors are linearly dependent

(iii) Three non-zero, non-coplanar vectors are linearly independent.

(iv) Three coplanar vectors are linearly dependent

(v) Any four vectors in 3-D space are linearly dependent.

You are urged to prove for yourself all these assertions.

**Example – 5**

Let \({\vec a},\vec b\,\,{\text{and}}\,\,\vec c\) be non-coplanar vectors. Are the vectors \(2\vec a - \vec b + 3\vec c,\,\,\vec a + \vec b - 2\vec c\,\,{\text{and}}\,\,\vec a + \vec b - 3\vec c\) coplanar or non-coplanar?

**Solution:** Three vectors are coplanar if there exist scalars \(\lambda ,\mu \in \mathbb{R}\) using which one vector can be expressed as the linear combination of the other two.

Let us try to find such scalars:

\[2\vec a - \vec b + 3\vec c = \lambda \left( {\vec a + \vec b - 2\vec c} \right) + \mu \left( {\vec a + \vec b - 3\vec c} \right)\]

\[ \Rightarrow \quad \left( {2 - \lambda - \mu } \right)\vec a + \left( { - 1 - \lambda - \mu } \right)\vec b + \left( {3 + 2\lambda + 3\mu } \right)\vec c = \vec 0\]

Since \(\vec a,\vec b,\vec c,\) are non-coplanar, we must have

\[2 - \lambda - \mu = 0\]

\[ - 1 - \lambda - \mu = 0\]

\[3 + 2\lambda + 3\mu = 0\]

This system, as can be easily verified , does not have a solution for \(\lambda \,\,{\text{and}}\,\,\mu \).

Thus, we cannot find scalars for which one vector can be expressed as the linear combination of the other two, implying the three vectors must be non-coplanar.

As an additional exercise, show that for three non-coplanar vectors \(\vec a,\,\,\vec b\,\,{\text{and}}\,\,\vec c\) , the vectors \(\vec a - \,2\vec b\, + 3\vec c,\,\,\vec a - 3\vec b + 5\vec c\,\,\,and\,\,\, - 2\vec a - \,3\vec b\, - 4\vec c\) are coplanar.

## What is basis and dimension?

- An important result in linear algebra is the following: Every basis for V has the same number of vectors. The number of vectors in a basis for V is called the dimension of V, denoted by dim(V).

## What is a basis of a vector?

- A vector basis of a vector space is defined as a subset of vectors in that are linearly independent and span

## What is the dimensions of a set of vectors?

- Dimension of a Vector Space If V is spanned by a finite set, then V is said to be finite-dimensional, and the dimension of V, written as dim V, is the number of vectors in a basis for V. The dimension of the zero vector space {0} is defined to be 0.

## How to find the angle between two vectors in 3d?

- a. Calculate the magnitude of the vectors.
- b. Divide each vector by its vector magnitude to compute its unit vector.
- c. Compute the dot product of the unit vectors.
- d. Take the arcosine of the dot product of the unit vectors to get the angle between the vectors in radians.

## What is the angle between two vectors?

- The angle between two vectors, deferred by a single point, called the shortest angle at which you have to turn around one of the vectors to the position of co-directional with another vector.